| Copyright | (c) Adam Scibior 2015-2020 |
|---|---|
| License | MIT |
| Maintainer | leonhard.markert@tweag.io |
| Stability | experimental |
| Portability | GHC |
| Safe Haskell | None |
| Language | Haskell2010 |
Control.Monad.Bayes.Inference.SMC
Description
Sequential Monte Carlo (SMC) sampling.
Arnaud Doucet and Adam M. Johansen. 2011. A tutorial on particle filtering and smoothing: fifteen years later. In The Oxford Handbook of Nonlinear Filtering, Dan Crisan and Boris Rozovskii (Eds.). Oxford University Press, Chapter 8.
Synopsis
- sir :: Monad m => (forall x. Population m x -> Population m x) -> Int -> Int -> Sequential (Population m) a -> Population m a
- smcMultinomial :: MonadSample m => Int -> Int -> Sequential (Population m) a -> Population m a
- smcSystematic :: MonadSample m => Int -> Int -> Sequential (Population m) a -> Population m a
- smcMultinomialPush :: MonadInfer m => Int -> Int -> Sequential (Population m) a -> Population m a
- smcSystematicPush :: MonadInfer m => Int -> Int -> Sequential (Population m) a -> Population m a
Documentation
Arguments
| :: Monad m | |
| => (forall x. Population m x -> Population m x) | resampler |
| -> Int | number of timesteps |
| -> Int | population size |
| -> Sequential (Population m) a | model |
| -> Population m a |
Sequential importance resampling. Basically an SMC template that takes a custom resampler.
Arguments
| :: MonadSample m | |
| => Int | number of timesteps |
| -> Int | number of particles |
| -> Sequential (Population m) a | model |
| -> Population m a |
Sequential Monte Carlo with multinomial resampling at each timestep. Weights are not normalized.
Arguments
| :: MonadSample m | |
| => Int | number of timesteps |
| -> Int | number of particles |
| -> Sequential (Population m) a | model |
| -> Population m a |
Sequential Monte Carlo with systematic resampling at each timestep. Weights are not normalized.
Arguments
| :: MonadInfer m | |
| => Int | number of timesteps |
| -> Int | number of particles |
| -> Sequential (Population m) a | model |
| -> Population m a |
Sequential Monte Carlo with multinomial resampling at each timestep. Weights are normalized at each timestep and the total weight is pushed as a score into the transformed monad.
Arguments
| :: MonadInfer m | |
| => Int | number of timesteps |
| -> Int | number of particles |
| -> Sequential (Population m) a | model |
| -> Population m a |
Sequential Monte Carlo with systematic resampling at each timestep. Weights are normalized at each timestep and the total weight is pushed as a score into the transformed monad.